ecc-dreamplace

ecc-dreamplace is an ECC-integrated placement engine based on DREAMPlace. It keeps the GPU/CPU analytical placement foundation from DREAMPlace and extends it for the ECC physical-design data flow, differentiable timing analysis, timing-aware net weighting, and ECC early-global-routing driven routability optimization.

This repository is packaged as a Python wheel for the ECOS Studio silicon design platform. The original upstream DREAMPlace README is preserved in README_DREAMPlace.md, and the inherited AutoDMP notes are preserved in README_AutoDMP.md.

What Is New Compared with DREAMPlace?

ECC Data-Flow Integration

ecc-dreamplace can run placement directly from the ECC data flow instead of requiring a standalone DREAMPlace benchmark conversion path.

• dreamplace/Placer.py exposes Placer.setup_rawdb(ecc_module) to initialize DREAMPlace from an ECC module.
• dreamplace/macroPlaceDB.py builds the Python placement database from ECC data via ecc_module.pydb(...).
• Placement results can be written back through ECC with ecc_module.write_placement_back(...) / ecc_module.def_save(...).

PyTorch-Based Differentiable STA

The repository adds a PyTorch-based static timing analysis path that supports both forward timing evaluation and backward timing-gradient computation.

The timing path includes:

• Steiner topology construction in dreamplace/ops/steiner_topo/.
• Elmore-delay net modeling in dreamplace/ops/rc_timing/.
• Timing-graph propagation in dreamplace/ops/timing_propagation/.
• Integration with the placement objective in dreamplace/PlaceObj.py.

With with_sta enabled, the placer builds timing propagation and Elmore-delay operators during placement initialization. The timing objective computes Steiner topology, net delay, slew/load propagation, and WNS/TNS-style timing metrics in the PyTorch computation graph.

Timing-Driven Placement and Net Weighting

The repository implements timing-aware controls commonly used in recent timing-driven placement flows, including net-level and pin-to-pin weighting. The main configuration knobs are:

Parameter	Purpose
enable_net_weighting	Enable timing-aware net weighting during global placement.
net_weighting_scheme	Select the net-weighting scheme, currently configured for options such as adam and lilith.
max_net_weight	Cap timing-driven net weights, or use inf for no cap.
pin2pin_net_weighting	Enable pin-to-pin timing weighting.
pin2pin_weight	Base multiplier for pin-to-pin timing weights.
timing_eval_flag	Enable timing evaluation reporting.
risa_weights	Use RISA-style weighted smooth HPWL to improve correlation with routed/Steiner wirelength.

Note: timing_opt_flag is a legacy DREAMPlace/OpenTimer flag in this fork. It is intentionally marked as unsupported because the old OpenTimer integration has been removed. Use the ECC-integrated STA path controlled by with_sta, differentiable_timing_obj, and the net-weighting parameters above.

ECC EGR-Based Routability Inflation

ecc-dreamplace supports routability-driven cell inflation using ECC/iRT early global routing feedback.

• dreamplace/ops/irt_egr/ wraps the ECC/iRT early global routing path.
• dreamplace/PlaceObj.py builds irt_egr_congestion_map_op when routability optimization is enabled.
• dreamplace/ops/adjust_node_area/ inflates movable-cell areas from route and pin utilization maps.

Relevant configuration knobs include:

Parameter	Purpose
routability_opt_flag	Enable routability-driven global placement.
adjust_nctugr_area_flag	Use the integrated EGR/NCTUgr-style route map for route-area adjustment.
adjust_rudy_area_flag	Use RUDY-style route utilization for route-area adjustment.
route_num_bins_x, route_num_bins_y	Routing-utilization grid resolution.
max_route_opt_adjust_rate	Maximum route-driven area inflation rate.
route_opt_adjust_exponent	Exponent applied to the route utilization map before inflation.
route_area_adjust_stop_ratio	Stop threshold for route-area inflation.

Build

Prerequisites

• Linux x86_64
• Python 3.11 + uv
• Optional: Nix, for entering the repository development shell before sync
• System packages: cmake ninja-build build-essential pkg-config libboost-all-dev libcairo2-dev libgflags-dev libgoogle-glog-dev flex libfl-dev bison libeigen3-dev libgtest-dev

Dev Setup

# If Nix is available, enter the dev shell first.
nix develop

# Sync the editable development environment.
uv sync --no-build-isolation-package ecc-dreamplace --verbose
source .venv/bin/activate

If Nix is not available, skip nix develop and run the uv sync command in the normal shell after installing the system packages above.

The package uses scikit-build editable rebuilds. Source edits are picked up on the next import, and native extensions rebuild automatically when needed.

Build Package

uv build

Output:

dist/ecc_dreamplace-*

The uv build runs the package build defined by pyproject.toml.

Repository Pointers

Path	Description
dreamplace/Placer.py	Top-level placer interface and ECC raw database setup.
dreamplace/macroPlaceDB.py	ECC-backed placement database construction and write-back.
dreamplace/PlaceObj.py	Placement objective, differentiable timing integration, and routing-inflation ops.
dreamplace/ops/steiner_topo/	Steiner topology operator.
dreamplace/ops/rc_timing/	Elmore-delay and RC timing operators.
dreamplace/ops/timing_propagation/	Timing-graph propagation operator.
dreamplace/ops/irt_egr/	ECC/iRT early-global-routing congestion-map wrapper.
dreamplace/ops/adjust_node_area/	Route/pin utilization driven cell-area inflation.
dreamplace/params.json	Full parameter schema and defaults.
docs/release.md	Release workflow.

Release

Releases are triggered by a version-bump PR and are published as GitHub release wheels. See docs/release.md.

References

If you use this repository, please also cite the relevant upstream and related works:

• Y. Lin, S. Dhar, W. Li, H. Ren, B. Khailany, and D. Z. Pan, "DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement," DAC 2019. [NVIDIA Research]
• P. Liao, D. Guo, Z. Guo, S. Liu, Y. Lin, and B. Yu, "DREAMPlace 4.0: Timing-Driven Placement With Momentum-Based Net Weighting and Lagrangian-Based Refinement," IEEE TCAD, 2023. [DOI: 10.1109/TCAD.2023.3240132]
• Z. Guo and Y. Lin, "Differentiable-Timing-Driven Global Placement," DAC 2022. [DOI: 10.1145/3489517.3530486] [PDF]
• Y. Shi, S. Xu, S. Kai, X. Lin, K. Xue, M. Yuan, and C. Qian, "Timing-Driven Global Placement by Efficient Critical Path Extraction," DATE 2025. [DOI: 10.23919/DATE64628.2025.10993273] [PDF] [Code]
• A. Agnesina, P. Rajvanshi, T. Yang, G. Pradipta, A. Jiao, B. Keller, B. Khailany, and H. Ren, "AutoDMP: Automated DREAMPlace-based Macro Placement," ISPD 2023. [NVIDIA Research]
• iEDA project, "iEDA: An Open-Source Intelligent Physical Implementation Toolkit and Library," 2023. [arXiv]

Contact

For questions about this ECC-integrated DREAMPlace fork, contact:

• Xueyan Zhao: zhaoxueyan21b@ict.ac.cn